OpenRaiser/Intern-Atlas huggingface dataset:
Quality Sensitive Price Competition in Spectrum Oligopoly:Part 1
Venue: arXiv cs.GT 2015
Year: 2015

We investigate a spectrum oligopoly market where primaries lease their channels to secondaries in lieu of financial remuneration. Transmission quality of a channel evolves randomly. Each primary has to select the price it would quote without knowing the transmission qualities of its competitors' channels. Each secondary buys a channel depending on the price and the transmission quality a channel offers. We formulate the price selection problem as a non co-operative game with primaries as players. In the one-shot game, we show that there exists a unique symmetric Nash Equilibrium(NE) strategy profile and explicitly compute it. Our analysis reveals that under the NE strategy profile a primary prices its channel to render high quality channel more preferable to the secondary; this negates the popular belief that prices ought to be selected to render channels equally preferable to the secondary regardless of their qualities. We show the loss of revenue in the asymptotic limit due to the non co-operation of primaries. In the repeated version of the game, we characterize a subgame perfect NE where a primary can attain a payoff arbitrarily close to the payoff it would obtain when primaries co-operate.
Tight bounds for learning a mixture of two gaussians
Venue: arXiv cs.LG 2015
Year: 2015

We consider the problem of identifying the parameters of an unknown mixture of two arbitrary $d$-dimensional gaussians from a sequence of independent random samples. Our main results are upper and lower bounds giving a computationally efficient moment-based estimator with an optimal convergence rate, thus resolving a problem introduced by Pearson (1894). Denoting by $σ^2$ the variance of the unknown mixture, we prove that $Θ(σ^{12})$ samples are necessary and sufficient to estimate each parameter up to constant additive error when $d=1.$ Our upper bound extends to arbitrary dimension $d>1$ up to a (provably necessary) logarithmic loss in $d$ using a novel---yet simple---dimensionality reduction technique. We further identify several interesting special cases where the sample complexity is notably smaller than our optimal worst-case bound. For instance, if the means of the two components are separated by $Ω(σ)$ the sample complexity reduces to $O(σ^2)$ and this is again optimal. Our results also apply to learning each component of the mixture up to small error in total variation distance, where our algorithm gives strong improvements in sample complexity over previous work. We also extend our lower bound to mixtures of $k$ Gaussians, showing that $Ω(σ^{6k-2})$ samples are necessary to estimate each parameter up to constant additive error.
Directional Cell Discovery in Millimeter Wave Cellular Networks
Venue: arXiv cs.IT 2015
Year: 2015

The acute disparity between increasing bandwidth demand and available spectrum, has brought millimeter wave (mmW) bands to the forefront of candidate solutions for the next-generation cellular networks. Highly directional transmissions are essential for cellular communication in these frequencies to compensate for high isotropic path loss. This reliance on directional beamforming, however, complicates initial cell search since the mobile and base station must jointly search over a potentially large angular directional space to locate a suitable path to initiate communication. To address this problem, this paper proposes a directional cell discovery procedure where base stations periodically transmit synchronization signals, potentially in time-varying random directions, to scan the angular space. Detectors for these signals are derived based on a Generalized Likelihood Ratio Test (GLRT) under various signal and receiver assumptions. The detectors are then simulated under realistic design parameters and channels based on actual experimental measurements at 28~GHz in New York City. The study reveals two key findings: (i) digital beamforming can significantly outperform analog beamforming even when the digital beamforming uses very low quantization to compensate for the additional power requirements; and (ii) omni-directional transmissions of the synchronization signals from the base station generally outperforms random directional scanning.
Reducing Clocks in Timed Automata while Preserving Bisimulation
Venue: arXiv cs.FL 2015
Year: 2015

Model checking timed automata becomes increasingly complex with the increase in the number of clocks. Hence it is desirable that one constructs an automaton with the minimum number of clocks possible. The problem of checking whether there exists a timed automaton with a smaller number of clocks such that the timed language accepted by the original automaton is preserved is known to be undecidable. In this paper, we give a construction, which for any given timed automaton produces a timed bisimilar automaton with the least number of clocks. Further, we show that such an automaton with the minimum possible number of clocks can be constructed in time that is doubly exponential in the number of clocks of the original automaton.
Subspace Codes based on Graph Matchings, Ferrers Diagrams and Pending Blocks
Venue: arXiv cs.IT 2015
Year: 2015

This paper provides new constructions and lower bounds for subspace codes, using Ferrers diagram rank-metric codes from matchings of the complete graph and pending blocks. We present different constructions for constant dimension codes with minimum injection distance $2$ or $k-1$, where $k$ is the constant dimension. Furthermore, we present a construction of new codes from old codes for any minimum distance. Then we construct non-constant dimension codes from these codes. The examples of codes obtained by these constructions are the largest known codes for the given parameters.
vVote: a Verifiable Voting System
Venue: arXiv cs.CR 2015
Year: 2015

The Pret a Voter cryptographic voting system was designed to be flexible and to offer voters a familiar and easy voting experience. In this paper we present a case study of our efforts to adapt Pret a Voter to the idiosyncrasies of elections in the Australian state of Victoria. This technical report includes general background, user experience and details of the cryptographic protocols and human processes. We explain the problems, present solutions, then analyse their security properties and explain how they tie in to other design decisions. We hope this will be an interesting case study on the application of end-to-end verifiable voting protocols to real elections. A preliminary version of this paper appeared as the 10th February 2014 version of "Draft Technical Report for VEC vVote System". The team involved in developing the vVote design described in this report were: Craig Burton, Chris Culnane, James Heather, Rui Joaquim, Peter Y. A. Ryan, Steve Schneider and Vanessa Teague.
Formal Proofs for Nonlinear Optimization
Venue: arXiv cs.LO 2015
Year: 2015

We present a formally verified global optimization framework. Given a semialgebraic or transcendental function $f$ and a compact semialgebraic domain $K$, we use the nonlinear maxplus template approximation algorithm to provide a certified lower bound of $f$ over $K$. This method allows to bound in a modular way some of the constituents of $f$ by suprema of quadratic forms with a well chosen curvature. Thus, we reduce the initial goal to a hierarchy of semialgebraic optimization problems, solved by sums of squares relaxations. Our implementation tool interleaves semialgebraic approximations with sums of squares witnesses to form certificates. It is interfaced with Coq and thus benefits from the trusted arithmetic available inside the proof assistant. This feature is used to produce, from the certificates, both valid underestimators and lower bounds for each approximated constituent. The application range for such a tool is widespread; for instance Hales' proof of Kepler's conjecture yields thousands of multivariate transcendental inequalities. We illustrate the performance of our formal framework on some of these inequalities as well as on examples from the global optimization literature.
On the metric s-t path Traveling Salesman Problem
Venue: arXiv cs.DS 2015
Year: 2015

We study the metric $s$-$t$ path Traveling Salesman Problem (TSP). [An, Kleinberg, and Shmoys, STOC 2012] improved on the long standing $\frac{5}{3}$-approximation factor and presented an algorithm that achieves an approximation factor of $\frac{1+\sqrt{5}}{2}\approx1.61803$. Later [Sebő, IPCO 2013] further improved the approximation factor to $\frac{8}{5}$. We present a simple, self-contained analysis that unifies both results; our main contribution is a \emph{unified correction vector}. We compare two different linear programming (LP) relaxations of the $s$-$t$ path TSP, namely, the path version of the Held-Karp LP relaxation for TSP and a weaker LP relaxation, and we show that both LPs have the same (fractional) optimal value. Also, we show that the minimum-cost of integral solutions of the two LPs are within a factor of $\frac{3}{2}$ of each other. We prove that a half-integral solution of the stronger LP-relaxation of cost $c$ can be rounded to an integral solution of cost at most $\frac{3}{2}c$. Additionally, we give a bad instance that presents obstructions to two obvious methods that aim for an approximation factor of $\frac{3}{2}$.
Subspace Polynomials and Cyclic Subspace Codes
Venue: arXiv cs.IT 2015
Year: 2015

Subspace codes have received an increasing interest recently due to their application in error-correction for random network coding. In particular, cyclic subspace codes are possible candidates for large codes with efficient encoding and decoding algorithms. In this paper we consider such cyclic codes and provide constructions of optimal codes for which their codewords do not have full orbits. We further introduce a new way to represent subspace codes by a class of polynomials called subspace polynomials. We present some constructions of such codes which are cyclic and analyze their parameters.
Microsoft COCO: Common Objects in Context
Venue: arXiv cs.CV 2015
Year: 2015

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.
Near Optimal Energy Control and Approximate Capacity of Energy Harvesting Communication
Venue: arXiv cs.IT 2015
Year: 2015

We consider an energy-harvesting communication system where a transmitter powered by an exogenous energy arrival process and equipped with a finite battery of size $B_{max}$ communicates over a discrete-time AWGN channel. We first concentrate on a simple Bernoulli energy arrival process where at each time step, either an energy packet of size $E$ is harvested with probability $p$, or no energy is harvested at all, independent of the other time steps. We provide a near optimal energy control policy and a simple approximation to the information-theoretic capacity of this channel. Our approximations for both problems are universal in all the system parameters involved ($p$, $E$ and $B_{max}$), i.e. we bound the approximation gaps by a constant independent of the parameter values. Our results suggest that a battery size $B_{max}\geq E$ is (approximately) sufficient to extract the infinite battery capacity of this channel. We then extend our results to general i.i.d. energy arrival processes. Our approximate capacity characterizations provide important insights for the optimal design of energy harvesting communication systems in the regime where both the battery size and the average energy arrival rate are large.
Adaptation Algorithm and Theory Based on Generalized Discrepancy
Venue: arXiv cs.LG 2015
Year: 2015

We present a new algorithm for domain adaptation improving upon a discrepancy minimization algorithm previously shown to outperform a number of algorithms for this task. Unlike many previous algorithms for domain adaptation, our algorithm does not consist of a fixed reweighting of the losses over the training sample. We show that our algorithm benefits from a solid theoretical foundation and more favorable learning bounds than discrepancy minimization. We present a detailed description of our algorithm and give several efficient solutions for solving its optimization problem. We also report the results of several experiments showing that it outperforms discrepancy minimization.
Upper Tail Estimates with Combinatorial Proofs
Venue: arXiv cs.DM 2015
Year: 2015

We study generalisations of a simple, combinatorial proof of a Chernoff bound similar to the one by Impagliazzo and Kabanets (RANDOM, 2010). In particular, we prove a randomized version of the hitting property of expander random walks and apply it to obtain a concentration bound for expander random walks which is essentially optimal for small deviations and a large number of steps. At the same time, we present a simpler proof that still yields a "right" bound settling a question asked by Impagliazzo and Kabanets. Next, we obtain a simple upper tail bound for polynomials with input variables in $[0, 1]$ which are not necessarily independent, but obey a certain condition inspired by Impagliazzo and Kabanets. The resulting bound is used by Holenstein and Sinha (FOCS, 2012) in the proof of a lower bound for the number of calls in a black-box construction of a pseudorandom generator from a one-way function. We then show that the same technique yields the upper tail bound for the number of copies of a fixed graph in an Erdős-Rényi random graph, matching the one given by Janson, Oleszkiewicz and Ruciński (Israel J. Math, 2002).
FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test
Venue: arXiv cs.AI 2015
Year: 2015

The maximum mean discrepancy (MMD) is a recently proposed test statistic for two-sample test. Its quadratic time complexity, however, greatly hampers its availability to large-scale applications. To accelerate the MMD calculation, in this study we propose an efficient method called FastMMD. The core idea of FastMMD is to equivalently transform the MMD with shift-invariant kernels into the amplitude expectation of a linear combination of sinusoid components based on Bochner's theorem and Fourier transform (Rahimi & Recht, 2007). Taking advantage of sampling of Fourier transform, FastMMD decreases the time complexity for MMD calculation from $O(N^2 d)$ to $O(L N d)$, where $N$ and $d$ are the size and dimension of the sample set, respectively. Here $L$ is the number of basis functions for approximating kernels which determines the approximation accuracy. For kernels that are spherically invariant, the computation can be further accelerated to $O(L N \log d)$ by using the Fastfood technique (Le et al., 2013). The uniform convergence of our method has also been theoretically proved in both unbiased and biased estimates. We have further provided a geometric explanation for our method, namely ensemble of circular discrepancy, which facilitates us to understand the insight of MMD, and is hopeful to help arouse more extensive metrics for assessing two-sample test. Experimental results substantiate that FastMMD is with similar accuracy as exact MMD, while with faster computation speed and lower variance than the existing MMD approximation methods.
Secure Fragmentation for Content-Centric Networks (extended version)
Venue: arXiv cs.NI 2015
Year: 2015

Content-Centric Networking (CCN) is a communication paradigm that emphasizes content distribution. Named-Data Networking (NDN) is an instantiation of CCN, a candidate Future Internet Architecture. NDN supports human-readable content naming and router-based content caching which lends itself to efficient, secure, and scalable content distribution. Because of NDN's fundamental requirement that each content object must be signed by its producer, fragmentation has been considered incompatible with NDN since it precludes authentication of individual content fragments by routers. The alternative is to perform hop-by-hop reassembly, which incurs prohibitive delays. In this paper, we show that secure and efficient content fragmentation is both possible and even advantageous in NDN and similar content-centric network architectures that involve signed content. We design a concrete technique that facilitates efficient and secure content fragmentation in NDN, discuss its security guarantees and assess performance. We also describe a prototype implementation and compare performance of cut-through with hop-by-hop fragmentation and reassembly.
Information Relaxations and Dynamic Zero-Sum Games
Venue: arXiv cs.GT 2015
Year: 2015

Dynamic zero-sum games are an important class of problems with applications ranging from evasion-pursuit and heads-up poker to certain adversarial versions of control problems such as multi-armed bandit and multiclass queuing problems. These games are generally very difficult to solve even when one player's strategy is fixed, and so constructing and evaluating good sub-optimal policies for each player is an important practical problem. In this paper, we propose the use of information relaxations to construct dual lower and upper bounds on the optimal value of the game. We note that the information relaxation approach, which has been developed and applied successfully to many large-scale dynamic programming problems, applies immediately to zero-sum game problems. We provide some simple numerical examples and identify interesting issues and complications that arise in the context of zero-sum games.
A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising
Venue: arXiv cs.GT 2015
Year: 2015

There are two major ways of selling impressions in display advertising. They are either sold in spot through auction mechanisms or in advance via guaranteed contracts. The former has achieved a significant automation via real-time bidding (RTB); however, the latter is still mainly done over the counter through direct sales. This paper proposes a mathematical model that allocates and prices the future impressions between real-time auctions and guaranteed contracts. Under conventional economic assumptions, our model shows that the two ways can be seamless combined programmatically and the publisher's revenue can be maximized via price discrimination and optimal allocation. We consider advertisers are risk-averse, and they would be willing to purchase guaranteed impressions if the total costs are less than their private values. We also consider that an advertiser's purchase behavior can be affected by both the guaranteed price and the time interval between the purchase time and the impression delivery date. Our solution suggests an optimal percentage of future impressions to sell in advance and provides an explicit formula to calculate at what prices to sell. We find that the optimal guaranteed prices are dynamic and are non-decreasing over time. We evaluate our method with RTB datasets and find that the model adopts different strategies in allocation and pricing according to the level of competition. From the experiments we find that, in a less competitive market, lower prices of the guaranteed contracts will encourage the purchase in advance and the revenue gain is mainly contributed by the increased competition in future RTB. In a highly competitive market, advertisers are more willing to purchase the guaranteed contracts and thus higher prices are expected. The revenue gain is largely contributed by the guaranteed selling.
Improving Energy Efficiency in Upstream EPON Channels by Packet Coalescing
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, we research the feasibility of adapting the packet coalescing algorithm, used successfully in IEEE 802.3az Ethernet cards, to upstream EPON channels. Our simulation experiments show that, using this algorithm, great power savings are feasible without requiring any changes to the deployed access network infrastructure nor to protocols.
TweetCred: Real-Time Credibility Assessment of Content on Twitter
Venue: arXiv cs.CR 2015
Year: 2015

During sudden onset crisis events, the presence of spam, rumors and fake content on Twitter reduces the value of information contained on its messages (or "tweets"). A possible solution to this problem is to use machine learning to automatically evaluate the credibility of a tweet, i.e. whether a person would deem the tweet believable or trustworthy. This has been often framed and studied as a supervised classification problem in an off-line (post-hoc) setting. In this paper, we present a semi-supervised ranking model for scoring tweets according to their credibility. This model is used in TweetCred, a real-time system that assigns a credibility score to tweets in a user's timeline. TweetCred, available as a browser plug-in, was installed and used by 1,127 Twitter users within a span of three months. During this period, the credibility score for about 5.4 million tweets was computed, allowing us to evaluate TweetCred in terms of response time, effectiveness and usability. To the best of our knowledge, this is the first research work to develop a real-time system for credibility on Twitter, and to evaluate it on a user base of this size.
Optimal Downlink Power Allocation in Cellular Networks
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, we introduce a novel approach for power allocation in cellular networks. In our model, we use sigmoidal-like utility functions to represent different users' modulation schemes. Each utility function is a representation of the probability of successfully transmitted packets per unit of power consumed by a user, when using a certain modulation scheme. We consider power allocation with utility proportional fairness policy, where the fairness among users is in utility percentage i.e. percentage of successfully transmitted packets of the corresponding modulation scheme. We formulate our network utility maximization problem as a product of utilities of all users and prove that our power allocation optimization problem is convex and therefore the optimal solution is tractable. We present a distributed algorithm to allocate base station (BS) powers optimally with priority given to users running lower modulation schemes while ensuring non-zero power allocation to users running higher modulation schemes. Our algorithm prevents fluctuation in the power allocation process and is capable of traffic and modulation dependent pricing i.e. charges different price per unit power from different users depending in part on their modulation scheme and total power available at the BS. This is used to flatten traffic and decrease the service price for users.
Negotiation Games (with abstract)
Venue: arXiv cs.LO 2015
Year: 2015

Negotiations, a model of concurrency with multi party negotiation as primitive, have been recently introduced in arXiv:1307.2145, arXiv:1403.4958. We initiate the study of games for this model. We study coalition problems: can a given coalition of agents force that a negotiation terminates (resp. block the negotiation so that it goes on forever)?; can the coalition force a given outcome of the negotiation? We show that for arbitrary negotiations the problems are EXPTIME-complete. Then we show that for sound and deterministic or even weakly deterministic negotiations the problems can be solved in PTIME. Notice that the input of the problems is a negotiation, which can be exponentially more compact than its state space.